A simple edge-weighted image enhancement filter using wavelet scale products

Nakashizuka, Makoto; Aoki, Kengo; Nitta, Tohru

doi:10.1109/mwscas.2004.1353983

Cited by 5 publications

(3 citation statements)

References 3 publications

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“…Discrete wavelet transform is commonly applied for edge detections and contrast enhancements in various applications (Huang et al 2002;Nakashizuka et al 2004;Hatami et al 2005). Pesquet et al (1996) suggested using the stationary wavelet transform (SWT) algorithm to make the subband coefficients time invariant and to improve its power.…”

Section: Introductionmentioning

confidence: 99%

Multiscale anisotropic diffusion for ringing artifact suppression in geophysical deconvolution data

Zuo

Geng

2016

Earthq Sci

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Ringing artifact degradations always appear in the deconvolution of geophysical data. To address this problem, we propose a postprocessing approach to suppress ringing artifacts that uses a novel anisotropic diffusion based on a stationary wavelet transform (SWT) algorithm. In this paper, we discuss the ringing artifact suppression problem and analyze the characteristics of the deconvolution ringing artifact. The deconvolution data containing ringing artifacts are decomposed into different SWT subbands for analysis, and a new multiscale adaptive anisotropic filter is developed to suppress these degradations. Finally, we demonstrate the performance of the proposed method and describe the experiments in detail.

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Section: Introductionmentioning

confidence: 99%

Multiscale anisotropic diffusion for ringing artifact suppression in geophysical deconvolution data

Zuo

Geng

2016

Earthq Sci

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“…Unsharp masking [3]- [10] is the most ordinary method for radiographs' enhancement. In digital radiograph, the background information belongs to low frequencies while diagnostically relevant information dominates mainly high frequency.…”

Section: Introductionmentioning

confidence: 99%

Radiographs enhancement based on unsharp masking and Gray-level Grouping

Wang

Shi

et al. 2012

Proceedings of 2012 IEEE-EMBS International Conference on Biomedical and Health Informatics

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Contrast enhancement has played an important role in image processing. A variety of enhancement algorithms now existing mainly focus on the overall contrast of digital images while sometimes we may wish to know much about details such as the lung capillaries and so on. The unsharp masking image enhancement techniques allow enhancing very small structures but are not accessible for larger size objects.Gray-level Grouping (GLG) method is effective to optimize the overall enhancement. In addition, multi-scale decomposition of the images has been proven to be capable and efficient method to cope with digital images. So we combined all of the three methods to enhance the digital radiographs. The experiment shows that contrast and details have been improved greatly.

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“…Other researchers have since used wavelet-based multi-scale edge sets to guide the magnification of the detail coefficients (Laine et al, 1994) or have magnified the edge sets directly and used the enhanced edge sets to reconstruct images (Lu et al, 1994). Also, wavelet transform is used for edge and contrast enhancement using different techniques in various applications (Kenneth and Singer, 1991;Laine et al, 1995;Itoh et al, 1999;Huang et al, 2002;Nakashizuka et al, 2004;Hatami et al, 2005).…”

Section: Introductionmentioning

confidence: 99%